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Spurious correlation #4,660 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Votes for the Republican Presidential candidate in Wisconsin and the second variable is Biomass power generated in Taiwan.  The chart goes from 1989 to 2020, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As the Republican candidate gained support in Wisconsin, it sparked a surge in political fervor. This led to an unexpected ripple effect, as people across the globe, including in Taiwan, felt the 'power' of the Wisconsin votes. This, in turn, ignited a 'biomass' of interest and action in Taiwan's energy sector. It seems like the political heat from Wisconsin traveled all the way to Taiwan, creating a 'republican' demand for biomass power! Those Wisconsin votes must've really 'fired up' the energy enthusiasts in Taiwan, proving that when it comes to influencing biomass power, it's all about generating the right kind of 'political spark'!

Model: dalle-3
Prompt: Prompt: Generate an image depicting a surge of energy and excitement originating from Wisconsin voters casting their ballots for the Republican presidential candidate and traveling to Taiwan, where a group of energy enthusiasts is inspired to increase biomass power generation. The image should show a group of Wisconsin voters standing in long voting lines, extending across the frame. The focus should be on the excitement and energy emanating from the voters as they cast their ballots for the Republican candidate. In the background, the scene transitions to Taiwan, where a group of energy enthusiasts is gathered around a biomass power plant, discussing plans and taking action to increase biomass power generation. The illustration should depict the palpable influence of the Wisconsin votes, igniting a Republican demand for biomass power in Taiwan. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.
This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: Votes for the Republican Presidential candidate in Wisconsin and Biomass power generated in Taiwan. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: Minimalism a funny image you would see on reddit Cubism Scalar art Whimsical illustration
ChatGPT: a funny image you would see on reddit

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Votes for the Republican Presidential candidate in Wisconsin and Biomass power generated in Taiwan, in the following situation: As the Republican candidate gained support in Wisconsin, it sparked a surge in political fervor. This led to an unexpected ripple effect, as people across the globe, including in Taiwan, felt the 'power' of the Wisconsin votes. This, in turn, ignited a 'biomass' of interest and action in Taiwan's energy sector. It seems like the political heat from Wisconsin traveled all the way to Taiwan, creating a 'republican' demand for biomass power! Those Wisconsin votes must've really 'fired up' the energy enthusiasts in Taiwan, proving that when it comes to influencing biomass power, it's all about generating the right kind of 'political spark'!. Keep the description simple.
ChatGPT: A crowd of Wisconsin voters standing in lines at polling stations, casting their ballots for the Republican presidential candidate. As their votes are counted, a surge of energy and excitement emanates, traveling across the globe to Taiwan. In Taiwan, a group of energy enthusiasts is seen gathered around a biomass power plant, discussing plans and taking action to increase biomass power generation. The influence of the Wisconsin votes is palpable, igniting a Republican demand for biomass power in Taiwan.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ a funny image you would see on reddit ] and depicting this scene: A crowd of Wisconsin voters standing in lines at polling stations, casting their ballots for the Republican presidential candidate. As their votes are counted, a surge of energy and excitement emanates, traveling across the globe to Taiwan. In Taiwan, a group of energy enthusiasts is seen gathered around a biomass power plant, discussing plans and taking action to increase biomass power generation. The influence of the Wisconsin votes is palpable, igniting a Republican demand for biomass power in Taiwan. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Generate an image depicting a surge of energy and excitement originating from Wisconsin voters casting their ballots for the Republican presidential candidate and traveling to Taiwan, where a group of energy enthusiasts is inspired to increase biomass power generation. The image should show a group of Wisconsin voters standing in long voting lines, extending across the frame. The focus should be on the excitement and energy emanating from the voters as they cast their ballots for the Republican candidate. In the background, the scene transitions to Taiwan, where a group of energy enthusiasts is gathered around a biomass power plant, discussing plans and taking action to increase biomass power generation. The illustration should depict the palpable influence of the Wisconsin votes, igniting a Republican demand for biomass power in Taiwan.

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.

Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in Votes for the Republican Presidential candidate in Wisconsin caused Biomass power generated in Taiwan to increase. Include one good pun.

AI academic paper

(Because p < 0.01)
Red Votes and Pre-Fired Quotes: The Relationship Between GOP Ballots in Wisconsin and Biomass Watts in Taiwan

The Journal of Political Eclecticism

Jan 2024

Reminder: This paper is AI-generated. Not real!
Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.




Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You write titles and abstracts of goofy academic research papers.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, and add unexpected twists in your writing.

Please draft the title and abstract of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Biomass power generated in Taiwan. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Rhyme words in the title.

Your research team used data from MIT Election Data and Science Lab, Harvard Dataverse and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.9270643 and p < 0.01 for 1989 to 2020.

Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)

[[TITLE]]
Lorem ipsum dolor sit

[[ABSTRACT]]
Lorem ipsum dolor sit



ChatGPT:
[[TITLE]]
"Red Votes and Pre-Fired Quotes: The Relationship Between GOP Ballots in Wisconsin and Biomass Watts in Taiwan"

[[ABSTRACT]]
In this study, we delve into the unexpected and seemingly unrelated realms of political polls and energy production to uncover a surprising correlation that may leave you saying, "What's a Republican's favorite type of renewable energy? Ele-logs!" We utilize data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration to explore the connection between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan. Our analysis spans from 1989 to 2020, uncovering a correlation coefficient of 0.9270643 and a p-value of less than 0.01. One might think this correlation is like mixing apples with oranges, but our findings suggest otherwise, leaving us wondering, "What do you call a politically conservative tree? A stump-ublican!"


Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the introduction section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the introduction section of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Biomass power generated in Taiwan. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Here is the title and abstract of the paper:
[[TITLE]]
"Red Votes and Pre-Fired Quotes: The Relationship Between GOP Ballots in Wisconsin and Biomass Watts in Taiwan"

[[ABSTRACT]]
In this study, we delve into the unexpected and seemingly unrelated realms of political polls and energy production to uncover a surprising correlation that may leave you saying, "What's a Republican's favorite type of renewable energy? Ele-logs!" We utilize data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration to explore the connection between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan. Our analysis spans from 1989 to 2020, uncovering a correlation coefficient of 0.9270643 and a p-value of less than 0.01. One might think this correlation is like mixing apples with oranges, but our findings suggest otherwise, leaving us wondering, "What do you call a politically conservative tree? A stump-ublican!"

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

As political pundits debate the intricacies of voting patterns in Wisconsin, and energy enthusiasts pore over the statistics of biomass power generation in Taiwan, one might initially think these topics have about as much in common as a fish and a bicycle. However, our research aims to show that these seemingly disparate variables may be more entwined than one might expect, much like a good dad joke sneaking up on you when you least expect it.

In the world of research, discovering unexpected connections can be just as exciting as finding a five-dollar bill in the pocket of your lab coat. With the widespread interest in renewable energy and the persistent fascination with the political landscape, we sought to uncover any potential relationship between votes for the Republican presidential candidate in Wisconsin and biomass power generated in Taiwan that may leave you scratching your head and wondering, "What's a conservative tree's favorite song? 'Stump and Circumstance!'"

While some may argue that these variables have as much to do with each other as a kangaroo does with a cup of tea, our investigation aims to unveil any hidden threads that might tie together these seemingly distinct realms of politics and energy production. When embarking on this research, we recognized the potential for unexpected findings, akin to stumbling upon a lab experiment gone wrong and exclaiming, "Well, that's a hypothesis that went up in smoke faster than a pile of wood chips in a biomass power plant!"

The pursuit of knowledge often leads researchers down unconventional paths, and this study is no exception. Drawing on datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we conducted a thorough analysis, keen on uncovering any connection that may lurk beneath the surface. Much like the unexpected punchline of a well-crafted joke, we aim to reveal a correlation that may elicit a bemused "Biomass power and Republican votes? That's quite a twist, like a DNA strand doing the cha-cha!"


Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Biomass power generated in Taiwan. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.

Make up the lorem and ipsum part, but make it sound related to the topic at hand.

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
"Red Votes and Pre-Fired Quotes: The Relationship Between GOP Ballots in Wisconsin and Biomass Watts in Taiwan"

[[ABSTRACT]]
In this study, we delve into the unexpected and seemingly unrelated realms of political polls and energy production to uncover a surprising correlation that may leave you saying, "What's a Republican's favorite type of renewable energy? Ele-logs!" We utilize data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration to explore the connection between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan. Our analysis spans from 1989 to 2020, uncovering a correlation coefficient of 0.9270643 and a p-value of less than 0.01. One might think this correlation is like mixing apples with oranges, but our findings suggest otherwise, leaving us wondering, "What do you call a politically conservative tree? A stump-ublican!"

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
As political pundits debate the intricacies of voting patterns in Wisconsin, and energy enthusiasts pore over the statistics of biomass power generation in Taiwan, one might initially think these topics have about as much in common as a fish and a bicycle. However, our research aims to show that these seemingly disparate variables may be more entwined than one might expect, much like a good dad joke sneaking up on you when you least expect it.
In the world of research, discovering unexpected connections can be just as exciting as finding a five-dollar bill in the pocket of your lab coat. With the widespread interest in renewable energy and the persistent fascination with the political landscape, we sought to uncover any potential relationship between votes for the Republican presidential candidate in Wisconsin and biomass power generated in Taiwan that may leave you scratching your head and wondering, "What's a conservative tree's favorite song? 'Stump and Circumstance!'"
While some may argue that these variables have as much to do with each other as a kangaroo does with a cup of tea, our investigation aims to unveil any hidden threads that might tie together these seemingly distinct realms of politics and energy production. When embarking on this research, we recognized the potential for unexpected findings, akin to stumbling upon a lab experiment gone wrong and exclaiming, "Well, that's a hypothesis that went up in smoke faster than a pile of wood chips in a biomass power plant!"
The pursuit of knowledge often leads researchers down unconventional paths, and this study is no exception. Drawing on datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we conducted a thorough analysis, keen on uncovering any connection that may lurk beneath the surface. Much like the unexpected punchline of a well-crafted joke, we aim to reveal a correlation that may elicit a bemused "Biomass power and Republican votes? That's quite a twist, like a DNA strand doing the cha-cha!"

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

Previous research has delved into the curious interplay between political voting patterns and environmental factors, with a focus on understanding the potential correlations that may exist between seemingly unrelated variables. Smith et al. (2015) investigated the connection between renewable energy policies and voter behavior, while Doe (2018) explored the impact of political ideologies on attitudes towards biomass power generation. However, none of these studies quite anticipated the wild ride we're about to take through the zany world of Republican votes in Wisconsin and biomass power in Taiwan. It's like they took a detour and ended up at a comedy club, but forgot to tell us the punchline.

In "Renewable Energies and Political Etiquette," Smith et al. uncovered the nuanced dynamics shaping public perceptions of renewable energy sources, shedding light on the influence of political orientations on individual attitudes. Similarly, Doe (2018) delved into the intricate dance between political leanings and environmental policies, hinting at the potential for unexpected connections that might leave even the most savvy voter doing a double-take. These studies set the stage for our investigation, as we dive headfirst into the uncharted waters of political ballots and biomass watts, akin to embarking on a scavenger hunt only to find the treasure map leads to a stand-up comedy show.

Moreover, books such as "The Politics of Renewable Energy" by Jones (2017) and "Biomass Power: A Global Perspective" by Green (2019) have offered invaluable insights into the intersection of politics and energy production. Yet despite the wealth of knowledge provided by these reputable sources, none of them quite prepared us for the riotous rollercoaster of discovering a potential link between Republican votes in Wisconsin and biomass power in Taiwan. It's like walking into a library expecting to find scholarly articles, but stumbling upon a stand-up comedy night instead.

Drawing inspiration from the fictional realm, novels like "The Power of Politics" and "Biomass Ballots" have woven intricate tales that blur the lines between political intrigue and environmental forces, teasing at the potential for unforeseen connections that may leave readers both enlightened and chuckling at the unexpected absurdity of it all. Meanwhile, works like "Election in the Enchanted Forest" and "The Biomass Conspiracy" have playfully toyed with the notion of hidden relationships, much like accidentally finding a comedy script mixed in with serious political discourse.

Our literature review also encompassed a wide range of sources, including the labels of shampoo bottles, where we serendipitously stumbled upon the idea that perhaps the real source of political power lies in luscious, voluminous hair. Who would have thought that a shampoo bottle could hold the key to unlocking the mysteries of political votes and biomass power? It's like finding a punchline to a joke in the most unexpected of places.


Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Biomass power generated in Taiwan. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from MIT Election Data and Science Lab, Harvard Dataverse and Energy Information Administration . You used data from 1989 to 2020

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"Red Votes and Pre-Fired Quotes: The Relationship Between GOP Ballots in Wisconsin and Biomass Watts in Taiwan"

[[ABSTRACT]]
In this study, we delve into the unexpected and seemingly unrelated realms of political polls and energy production to uncover a surprising correlation that may leave you saying, "What's a Republican's favorite type of renewable energy? Ele-logs!" We utilize data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration to explore the connection between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan. Our analysis spans from 1989 to 2020, uncovering a correlation coefficient of 0.9270643 and a p-value of less than 0.01. One might think this correlation is like mixing apples with oranges, but our findings suggest otherwise, leaving us wondering, "What do you call a politically conservative tree? A stump-ublican!"

[[INTRODUCTION]]
As political pundits debate the intricacies of voting patterns in Wisconsin, and energy enthusiasts pore over the statistics of biomass power generation in Taiwan, one might initially think these topics have about as much in common as a fish and a bicycle. However, our research aims to show that these seemingly disparate variables may be more entwined than one might expect, much like a good dad joke sneaking up on you when you least expect it.
In the world of research, discovering unexpected connections can be just as exciting as finding a five-dollar bill in the pocket of your lab coat. With the widespread interest in renewable energy and the persistent fascination with the political landscape, we sought to uncover any potential relationship between votes for the Republican presidential candidate in Wisconsin and biomass power generated in Taiwan that may leave you scratching your head and wondering, "What's a conservative tree's favorite song? 'Stump and Circumstance!'"
While some may argue that these variables have as much to do with each other as a kangaroo does with a cup of tea, our investigation aims to unveil any hidden threads that might tie together these seemingly distinct realms of politics and energy production. When embarking on this research, we recognized the potential for unexpected findings, akin to stumbling upon a lab experiment gone wrong and exclaiming, "Well, that's a hypothesis that went up in smoke faster than a pile of wood chips in a biomass power plant!"
The pursuit of knowledge often leads researchers down unconventional paths, and this study is no exception. Drawing on datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we conducted a thorough analysis, keen on uncovering any connection that may lurk beneath the surface. Much like the unexpected punchline of a well-crafted joke, we aim to reveal a correlation that may elicit a bemused "Biomass power and Republican votes? That's quite a twist, like a DNA strand doing the cha-cha!"

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To unravel the enigmatic relationship between the voting behavior in the cheese-loving state of Wisconsin and the biomass energy scene in Taiwan, our research team embarked on a statistical odyssey that promised more surprises and unexpected connections than a magician's hat. We harnessed the power of data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, akin to mixing a political soufflé with a dash of renewable energy seasoning.

First, we navigated through the labyrinth of historical data, spanning from 1989 to 2020, like intrepid explorers in a quest for the elusive link between votes for the Republican presidential candidate in Wisconsin and the wattage of biomass power generated in Taiwan. This journey was filled with twists and turns, much like the plot of a classic mystery novel, but with fewer suspects and more correlation coefficients.

Our team constructed a methodological framework as sturdy as a brick house, employing rigorous statistical analyses to measure the relationship between these seemingly unrelated variables. We calculated regression models and applied time series analysis, ensuring that our methodology was as robust as a sturdy wind turbine in a biomass power plant.

Furthermore, we conducted a series of sensitivity analyses to ensure the reliability of our findings, akin to double-checking the ingredients in a recipe to avoid serving up a statistical soufflé that collapses at the first hint of scrutiny. We dotted our i's and crossed our t's, scrutinizing our data for outliers and anomalies, much like a keen-eyed detective poring over a crime scene for any overlooked breadcrumbs.

As we delved deeper into the data, we employed advanced statistical techniques that would make even the most seasoned research aficionado raise an eyebrow, much like finding a pineapple on a pizza – unexpected yet strangely intriguing. We employed Granger causality tests to determine the direction of any potential relationship, constructing a web of statistical evidence more intricate than a spider's silk spun by R programming.

Finally, we conducted a host of robustness checks to confirm the stability of our results, ensuring that they stood as firm as a sequoia tree in a Wisconsin forest. These checks were crucial to safeguard against any statistical shenanigans that might derail our findings, making sure that our conclusions were as steadfast as the conviction of a dad joke enthusiast extolling the virtues of puns at a family gathering.

In essence, our methodological approach combined the precision of a Swiss watch with the creativity of a surrealist painter, uncovering a surprising correlation that may just leave you pondering, "What's a statistician's favorite type of power? Empirical power!"


Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the results section of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Biomass power generated in Taiwan. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from MIT Election Data and Science Lab, Harvard Dataverse and Energy Information Administration .

For the time period 1989 to 2020, you found a correlation 0.9270643, r-squared of 0.8594482, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
"Red Votes and Pre-Fired Quotes: The Relationship Between GOP Ballots in Wisconsin and Biomass Watts in Taiwan"

[[ABSTRACT]]
In this study, we delve into the unexpected and seemingly unrelated realms of political polls and energy production to uncover a surprising correlation that may leave you saying, "What's a Republican's favorite type of renewable energy? Ele-logs!" We utilize data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration to explore the connection between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan. Our analysis spans from 1989 to 2020, uncovering a correlation coefficient of 0.9270643 and a p-value of less than 0.01. One might think this correlation is like mixing apples with oranges, but our findings suggest otherwise, leaving us wondering, "What do you call a politically conservative tree? A stump-ublican!"

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
As political pundits debate the intricacies of voting patterns in Wisconsin, and energy enthusiasts pore over the statistics of biomass power generation in Taiwan, one might initially think these topics have about as much in common as a fish and a bicycle. However, our research aims to show that these seemingly disparate variables may be more entwined than one might expect, much like a good dad joke sneaking up on you when you least expect it.
In the world of research, discovering unexpected connections can be just as exciting as finding a five-dollar bill in the pocket of your lab coat. With the widespread interest in renewable energy and the persistent fascination with the political landscape, we sought to uncover any potential relationship between votes for the Republican presidential candidate in Wisconsin and biomass power generated in Taiwan that may leave you scratching your head and wondering, "What's a conservative tree's favorite song? 'Stump and Circumstance!'"
While some may argue that these variables have as much to do with each other as a kangaroo does with a cup of tea, our investigation aims to unveil any hidden threads that might tie together these seemingly distinct realms of politics and energy production. When embarking on this research, we recognized the potential for unexpected findings, akin to stumbling upon a lab experiment gone wrong and exclaiming, "Well, that's a hypothesis that went up in smoke faster than a pile of wood chips in a biomass power plant!"
The pursuit of knowledge often leads researchers down unconventional paths, and this study is no exception. Drawing on datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we conducted a thorough analysis, keen on uncovering any connection that may lurk beneath the surface. Much like the unexpected punchline of a well-crafted joke, we aim to reveal a correlation that may elicit a bemused "Biomass power and Republican votes? That's quite a twist, like a DNA strand doing the cha-cha!"

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

Upon conducting our analysis, we discovered a noteworthy correlation between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan. The correlation coefficient of 0.9270643 and an r-squared value of 0.8594482 indicate a strong positive relationship between these seemingly unrelated variables. This result suggests that the higher the Republican votes in Wisconsin, the greater the biomass power generated in Taiwan. It's almost as if the conservative support in Wisconsin sends some kind of renewable energy vibe across the Pacific ocean. One might even ask, "What did the GOP say to the biomass power plant? 'I'm a huge fan of your work!'"

Fig. 1 visually encapsulates this surprising correlation, resembling a connect-the-dots puzzle that even the most skeptical statistician couldn't resist. The scatterplot showcases the data points aligning in a manner that would make any conspiracy theorist proud, displaying a clear positive trend reminiscent of a political candidate’s approval ratings during an election year. One could almost hear the data points whispering, "I'm with Her(biomass)!"

The statistical significance of this relationship, with a p-value of less than 0.01, provides strong evidence against the null hypothesis of no correlation. It's as if the data itself is saying, "Don't underestimate the power of biomass or Republican votes!"

Our findings challenge conventional wisdom and add an unexpected twist to the discourse surrounding political affiliations and energy production. It's like finding out that the energy industry and political ideologies have been secretly swapping recipes for sustainable living. One can't help but wonder, "What do you get when you cross a Republican voter with a biomass power plant? A conservative energy enthusiast!"

Overall, our results suggest an intriguing and possibly influential connection between votes for the Republican presidential candidate in Wisconsin and biomass power generated in Taiwan, leaving us with a newfound appreciation for the unpredictability of research outcomes. It's almost like science is trying to tell a wonderfully unexpected joke, and we can't help but appreciate the punchline, even if it leaves us groaning.


Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Biomass power generated in Taiwan. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
"Red Votes and Pre-Fired Quotes: The Relationship Between GOP Ballots in Wisconsin and Biomass Watts in Taiwan"

[[ABSTRACT]]
In this study, we delve into the unexpected and seemingly unrelated realms of political polls and energy production to uncover a surprising correlation that may leave you saying, "What's a Republican's favorite type of renewable energy? Ele-logs!" We utilize data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration to explore the connection between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan. Our analysis spans from 1989 to 2020, uncovering a correlation coefficient of 0.9270643 and a p-value of less than 0.01. One might think this correlation is like mixing apples with oranges, but our findings suggest otherwise, leaving us wondering, "What do you call a politically conservative tree? A stump-ublican!"

[[LITERATURE REVIEW]]
Previous research has delved into the curious interplay between political voting patterns and environmental factors, with a focus on understanding the potential correlations that may exist between seemingly unrelated variables. Smith et al. (2015) investigated the connection between renewable energy policies and voter behavior, while Doe (2018) explored the impact of political ideologies on attitudes towards biomass power generation. However, none of these studies quite anticipated the wild ride we're about to take through the zany world of Republican votes in Wisconsin and biomass power in Taiwan. It's like they took a detour and ended up at a comedy club, but forgot to tell us the punchline.
In "Renewable Energies and Political Etiquette," Smith et al. uncovered the nuanced dynamics shaping public perceptions of renewable energy sources, shedding light on the influence of political orientations on individual attitudes. Similarly, Doe (2018) delved into the intricate dance between political leanings and environmental policies, hinting at the potential for unexpected connections that might leave even the most savvy voter doing a double-take. These studies set the stage for our investigation, as we dive headfirst into the uncharted waters of political ballots and biomass watts, akin to embarking on a scavenger hunt only to find the treasure map leads to a stand-up comedy show.
Moreover, books such as "The Politics of Renewable Energy" by Jones (2017) and "Biomass Power: A Global Perspective" by Green (2019) have offered invaluable insights into the intersection of politics and energy production. Yet despite the wealth of knowledge provided by these reputable sources, none of them quite prepared us for the riotous rollercoaster of discovering a potential link between Republican votes in Wisconsin and biomass power in Taiwan. It's like walking into a library expecting to find scholarly articles, but stumbling upon a stand-up comedy night instead.
Drawing inspiration from the fictional realm, novels like "The Power of Politics" and "Biomass Ballots" have woven intricate tales that blur the lines between political intrigue and environmental forces, teasing at the potential for unforeseen connections that may leave readers both enlightened and chuckling at the unexpected absurdity of it all. Meanwhile, works like "Election in the Enchanted Forest" and "The Biomass Conspiracy" have playfully toyed with the notion of hidden relationships, much like accidentally finding a comedy script mixed in with serious political discourse.
Our literature review also encompassed a wide range of sources, including the labels of shampoo bottles, where we serendipitously stumbled upon the idea that perhaps the real source of political power lies in luscious, voluminous hair. Who would have thought that a shampoo bottle could hold the key to unlocking the mysteries of political votes and biomass power? It's like finding a punchline to a joke in the most unexpected of places.

[[RESULTS]]
Upon conducting our analysis, we discovered a noteworthy correlation between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan. The correlation coefficient of 0.9270643 and an r-squared value of 0.8594482 indicate a strong positive relationship between these seemingly unrelated variables. This result suggests that the higher the Republican votes in Wisconsin, the greater the biomass power generated in Taiwan. It's almost as if the conservative support in Wisconsin sends some kind of renewable energy vibe across the Pacific ocean. One might even ask, "What did the GOP say to the biomass power plant? 'I'm a huge fan of your work!'"
Fig. 1 visually encapsulates this surprising correlation, resembling a connect-the-dots puzzle that even the most skeptical statistician couldn't resist. The scatterplot showcases the data points aligning in a manner that would make any conspiracy theorist proud, displaying a clear positive trend reminiscent of a political candidate’s approval ratings during an election year. One could almost hear the data points whispering, "I'm with Her(biomass)!"
The statistical significance of this relationship, with a p-value of less than 0.01, provides strong evidence against the null hypothesis of no correlation. It's as if the data itself is saying, "Don't underestimate the power of biomass or Republican votes!"
Our findings challenge conventional wisdom and add an unexpected twist to the discourse surrounding political affiliations and energy production. It's like finding out that the energy industry and political ideologies have been secretly swapping recipes for sustainable living. One can't help but wonder, "What do you get when you cross a Republican voter with a biomass power plant? A conservative energy enthusiast!"
Overall, our results suggest an intriguing and possibly influential connection between votes for the Republican presidential candidate in Wisconsin and biomass power generated in Taiwan, leaving us with a newfound appreciation for the unpredictability of research outcomes. It's almost like science is trying to tell a wonderfully unexpected joke, and we can't help but appreciate the punchline, even if it leaves us groaning.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The results of our study have uncovered a surprising and robust correlation between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan, shedding light on an unexpected link that may have researchers and policymakers alike exclaiming, "Where there's a vote, there's a way – to biomass energy generation!" Our findings are in line with the prior research, daring us to reevaluate our assumptions and leaving us with a sense of scientific mischief that would make even the most distinguished scholar crack a smile.

Our discovery of a correlation coefficient of 0.9270643 between these disparate variables not only supports the prior literature's hints at potential unforeseen connections but also raises the question, "What did one variable say to the other? 'You're my significant other!" It seems that the dance of political ballots in Wisconsin and the whir of biomass watts in Taiwan perform an intricately choreographed routine, much like a statistical ballet that leaves us marveling at the elegance of their partnership.

The strength of this correlation, as indicated by the r-squared value of 0.8594482, reinforces the notion that the higher the Republican votes in Wisconsin, the greater the biomass power generated in Taiwan. It's as if the political landscape in Wisconsin whispers its support across the seas to the biomass power plants, saying, "You've got my vote!" This unexpected connection challenges us to rethink how we perceive the reach and influence of political sentiments, pushing us to acknowledge that a vote cast in one corner of the world can reverberate across oceans and influence energy production elsewhere. It's almost like witnessing a cosmic joke play out on the stage of statistical significance.

The statistical significance of our findings, with a p-value of less than 0.01, firmly establishes the legitimacy of this correlation. It's as if the data itself is nudging us, saying, "Now, this is something you can't ignore – like a punchline that demands a chuckle!" This level of significance reaffirms the influence of political votes in shaping energy production dynamics, slyly hinting that perhaps biomass power generation has an inherent affinity for political red.

Our study, while delving into the unexpected union of political ballots and biomass watts, has illuminated a connection that defies conventional expectations and leaves us marveling at the quirky surprises that science can uncover. It's almost as if the research process itself is whispering a clever, unexpected punchline – compelling us to recognize that even the most seemingly unrelated variables may hold hands and waltz into statistical significance. In the end, it's like science is keeping a jokester spirit alive, with each discovery serving as a delightful punchline in the grand narrative of research.

I look forward to incorporating this fun and light-hearted tone into the discussion section of your academic paper!


Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Biomass power generated in Taiwan. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Red Votes and Pre-Fired Quotes: The Relationship Between GOP Ballots in Wisconsin and Biomass Watts in Taiwan"

[[ABSTRACT]]
In this study, we delve into the unexpected and seemingly unrelated realms of political polls and energy production to uncover a surprising correlation that may leave you saying, "What's a Republican's favorite type of renewable energy? Ele-logs!" We utilize data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration to explore the connection between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan. Our analysis spans from 1989 to 2020, uncovering a correlation coefficient of 0.9270643 and a p-value of less than 0.01. One might think this correlation is like mixing apples with oranges, but our findings suggest otherwise, leaving us wondering, "What do you call a politically conservative tree? A stump-ublican!"

[[INTRDUCTION]]
As political pundits debate the intricacies of voting patterns in Wisconsin, and energy enthusiasts pore over the statistics of biomass power generation in Taiwan, one might initially think these topics have about as much in common as a fish and a bicycle. However, our research aims to show that these seemingly disparate variables may be more entwined than one might expect, much like a good dad joke sneaking up on you when you least expect it.
In the world of research, discovering unexpected connections can be just as exciting as finding a five-dollar bill in the pocket of your lab coat. With the widespread interest in renewable energy and the persistent fascination with the political landscape, we sought to uncover any potential relationship between votes for the Republican presidential candidate in Wisconsin and biomass power generated in Taiwan that may leave you scratching your head and wondering, "What's a conservative tree's favorite song? 'Stump and Circumstance!'"
While some may argue that these variables have as much to do with each other as a kangaroo does with a cup of tea, our investigation aims to unveil any hidden threads that might tie together these seemingly distinct realms of politics and energy production. When embarking on this research, we recognized the potential for unexpected findings, akin to stumbling upon a lab experiment gone wrong and exclaiming, "Well, that's a hypothesis that went up in smoke faster than a pile of wood chips in a biomass power plant!"
The pursuit of knowledge often leads researchers down unconventional paths, and this study is no exception. Drawing on datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we conducted a thorough analysis, keen on uncovering any connection that may lurk beneath the surface. Much like the unexpected punchline of a well-crafted joke, we aim to reveal a correlation that may elicit a bemused "Biomass power and Republican votes? That's quite a twist, like a DNA strand doing the cha-cha!"

[[RESULTS]]
Upon conducting our analysis, we discovered a noteworthy correlation between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan. The correlation coefficient of 0.9270643 and an r-squared value of 0.8594482 indicate a strong positive relationship between these seemingly unrelated variables. This result suggests that the higher the Republican votes in Wisconsin, the greater the biomass power generated in Taiwan. It's almost as if the conservative support in Wisconsin sends some kind of renewable energy vibe across the Pacific ocean. One might even ask, "What did the GOP say to the biomass power plant? 'I'm a huge fan of your work!'"
Fig. 1 visually encapsulates this surprising correlation, resembling a connect-the-dots puzzle that even the most skeptical statistician couldn't resist. The scatterplot showcases the data points aligning in a manner that would make any conspiracy theorist proud, displaying a clear positive trend reminiscent of a political candidate’s approval ratings during an election year. One could almost hear the data points whispering, "I'm with Her(biomass)!"
The statistical significance of this relationship, with a p-value of less than 0.01, provides strong evidence against the null hypothesis of no correlation. It's as if the data itself is saying, "Don't underestimate the power of biomass or Republican votes!"
Our findings challenge conventional wisdom and add an unexpected twist to the discourse surrounding political affiliations and energy production. It's like finding out that the energy industry and political ideologies have been secretly swapping recipes for sustainable living. One can't help but wonder, "What do you get when you cross a Republican voter with a biomass power plant? A conservative energy enthusiast!"
Overall, our results suggest an intriguing and possibly influential connection between votes for the Republican presidential candidate in Wisconsin and biomass power generated in Taiwan, leaving us with a newfound appreciation for the unpredictability of research outcomes. It's almost like science is trying to tell a wonderfully unexpected joke, and we can't help but appreciate the punchline, even if it leaves us groaning.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, our study has unveiled a surprising and robust correlation between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan. Our findings indicate that these seemingly unrelated variables are actually entwined like a genetics lab technician's headphones. One might say, "The connection between conservative ballots and renewable energy is as clear as day, much like an experiment with transparent test tubes!"

The correlation coefficient of 0.9270643 and the r-squared value of 0.8594482 highlight the strength of this relationship, affirming that when it comes to politics and renewable energy, there's more to the story than meets the eye. It's almost as if the GOP's support in Wisconsin sends a renewable energy signal across the Pacific Ocean – talk about political power generating literal power abroad! One could even joke, "Why did the biomass power plant break up with the wind turbine? It just couldn't handle the political tension!"

The visual representation of this correlation in Fig. 1 is like a connect-the-dots puzzle that reveals an unexpected image, much like finding a hidden punchline in a complex joke. This connection challenges traditional wisdom and enriches our understanding of the intertwining of political affiliations and energy production, leaving us exclaiming, "Who knew politics and sustainable energy were such a dynamic duo, like Batman and Robin fighting climate change!"

Furthermore, with a p-value of less than 0.01, our results offer strong evidence against the null hypothesis, making it clear that the relationship between Republican votes in Wisconsin and biomass power in Taiwan is no statistical fluke. It's as if the data itself is whispering, "I may be statistical, but I'm not your average variable!"

In light of these compelling findings, it's clear that no further research is needed in this area. We can firmly declare that the connection between votes for the Republican presidential candidate in Wisconsin and biomass power generated in Taiwan is as real as a lab beaker and as fascinating as a groundbreaking dad joke – leaving us both amazed and chuckling.


Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited Red Votes and Pre-Fired Quotes: The Relationship Between GOP Ballots in Wisconsin and Biomass Watts in Taiwan. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Political Eclecticism


Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Red Votes and Pre-Fired Quotes: The Relationship Between GOP Ballots in Wisconsin and Biomass Watts in Taiwan. Respond only with the name of a fictional organization.

ChatGPT:

Institute for Democratic Energy Studies


Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Red Votes and Pre-Fired Quotes: The Relationship Between GOP Ballots in Wisconsin and Biomass Watts in Taiwan with an abstract of In this study, we delve into the unexpected and seemingly unrelated realms of political polls and energy production to uncover a surprising correlation that may leave you saying, "What's a Republican's favorite type of renewable energy? Ele-logs!" We utilize data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration to explore the connection between votes for the Republican presidential candidate in Wisconsin and the biomass power generated in Taiwan. Our analysis spans from 1989 to 2020, uncovering a correlation coefficient of 0.9270643 and a p-value of less than 0.01. One might think this correlation is like mixing apples with oranges, but our findings suggest otherwise, leaving us wondering, "What do you call a politically conservative tree? A stump-ublican!

ChatGPT:

Wisconsin, GOP, Republican party, political polls, energy production, biomass, Taiwan, correlation, MIT Election Data and Science Lab, Harvard Dataverse, Energy Information Administration, correlation coefficient, p-value, renewable energy, ele-logs, politically conservative, stump-ublican

*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.



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Data details

Votes for the Republican Presidential candidate in Wisconsin
Detailed data title: Total votes cast for the Republican Presidential candidate in Wisconsin
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for the Republican Presidential candidate in Wisconsin

Biomass power generated in Taiwan
Detailed data title: Total biomass power generated in Taiwan in billion kWh
Source: Energy Information Administration
See what else correlates with Biomass power generated in Taiwan

Correlation r = 0.9270643 (Pearson correlation coefficient)
Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.

r2 = 0.8594482 (Coefficient of determination)
This means 85.9% of the change in the one variable (i.e., Biomass power generated in Taiwan) is predictable based on the change in the other (i.e., Votes for the Republican Presidential candidate in Wisconsin) over the 8 years from 1989 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00092. 0.0009176907598612951000000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.93 in 0.092% of random cases. Said differently, if you correlated 1,090 random variables Which I absolutely did.
with the same 7 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 7 because we have two variables measured over a period of 8 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.64, 0.99 ] 95% correlation confidence interval (using the Fisher z-transformation)
The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!


All values for the years included above: If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.
19921996200020042008201220162020
Votes for the Republican Presidential candidate in Wisconsin (Total votes)930855845029123728014781201262390141097014052801610180
Biomass power generated in Taiwan (Billion kWh)0.2752040.6908621.792723.34933.361233.41923.601973.75309




Why this works

  1. Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
  2. Lack of causal connection: There is probably Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
    no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
  3. Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
    p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
  4. Very low n: There are not many data points included in this analysis. Even if the p-value is high, we should be suspicious of using so few datapoints in a correlation.
  5. Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
    Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.




Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."


# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([930855,845029,1237280,1478120,1262390,1410970,1405280,1610180,])
array_2 = np.array([0.275204,0.690862,1.79272,3.3493,3.36123,3.4192,3.60197,3.75309,])
array_1_name = "Votes for the Republican Presidential candidate in Wisconsin"
array_2_name = "Biomass power generated in Taiwan"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)



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Correlation ID: 4660 · Black Variable ID: 26047 · Red Variable ID: 24024
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